Due to its non-negativity constraint, it has the wonderful property of decomposing a objects as an additive combination of (often very meaningful) parts. However, as with all unsupervised learning tasks, it is sensitive to the relative scale of different features.

The fundamental problem is that the informativeness of a feature need not be related to its scale. For example, when processing speech, the highest-energy components of a magnitude spectrogram are those of the least perceptual importance! So when NMF decides which information to discard into order to achieve a low-rank factorisation that minimises the error function, it can be the signal, not the noise, that is sacrificed. This problem is not unique to NMF, of course: PCA retains those dimensions of the sample cloud that have the greatest variance.

It is in general better to learn a feature representation jointly with the downstream task, so that the model learns to scale features according to their informativeness for the task. If NMF is for some reason still desirable, however, it is possible to better control the information loss by choosing an appropriate measure of the matrix factorisation error.

There are three common error functions used in NMF (all of which Bregman divergences): squared Euclidean, Kullback-Leibler (KL) and Itakura-Saito (IS). These are respectively quadratic, linear and invariant with respect to the feature scale. Thus, for example, NMF with the Euclidean error function gives strong preference to high-energy features, while NMF with the IS error function is agnostic to feature scale.